Hand Detection Module

This project is a Python module that utilizes MediaPipe and OpenCV for real-time hand detection and landmark recognition using a webcam. The module detects hand landmarks and highlights specific points, such as the tip of the thumb, on the video feed.

Table of Contents

• Introduction
• Features
• Installation
• Usage
• How It Works
• Limitations

Introduction

This hand detection module is designed for real-time detection and tracking of hand landmarks. It leverages MediaPipe's pre-trained hand detection model and OpenCV for video capture and processing.

Features

• Real-time hand detection using a webcam.
• Identification and marking of specific hand landmarks (e.g., thumb tip).
• Display of the video feed with overlayed hand landmarks and frames per second (FPS).
• Simple and efficient, with the ability to handle multiple hands.

Installation

To use this module, ensure you have Python installed along with the required libraries:

pip install opencv-python
pip install mediapipe

Usage

To run the hand detection module, execute the script:

python Hand_detection_module.py

The script will open your webcam and start detecting hand landmarks in real-time. Press 'x' to exit the application.

How It Works

1. Video Capture: The script captures video from the default webcam using OpenCV.
2. Frame Processing: Each frame is converted to RGB and processed by the MediaPipe Hands model to detect hand landmarks.
3. Landmark Detection: The detected landmarks are identified and their coordinates are calculated.
4. Landmark Highlighting: Specific landmarks (like the thumb tip) are highlighted on the video feed using colored circles.
5. FPS Calculation: The script calculates and displays the frames per second (FPS) to monitor performance.

Key Sections of the Code

• Hand Landmark Detection: The code uses mp.solutions.hands to detect hand landmarks and mp.solutions.drawing_utils to draw them on the video feed.
• Specific Landmark Highlighting: The tip of the thumb (landmark ID 4) is highlighted with a filled circle.
• FPS Display: The FPS is calculated based on the time difference between consecutive frames and displayed on the video feed.

Limitations

• Lighting Conditions: The accuracy of hand detection can be affected by poor lighting conditions.
• Single Camera Source: The script is designed to work with a single webcam source and may require modifications for other video inputs.
• Dependency on MediaPipe: This module heavily relies on the MediaPipe library, so any updates or changes in MediaPipe might require adjustments to the code.